The best prompt engineer I know told me he stopped writing prompts.
He said: "Prompts are maybe 5% of what makes AI actually useful. The other 95%? It's everything the model sees before you even ask a question."
If you're building AI features and still obsessing over prompt wording, you're optimizing the wrong thing.
In this episode, I break down context engineering—what it is, where the term comes from, and how product managers can own the context window without writing code.
**What you'll learn:**
- Why "know your user" is the foundation of context engineering
- The 3 types of retrieval: keyword, semantic, and graph RAG
- Why more context actually hurts performance (context rot)
- How to build evals that learn from future outcomes
- 5 actionable homework items you can start today
**People mentioned:**
- Simon Willison (AI Engineer, Creator of Datasette)
- Kevin Weil (CPO at OpenAI)
**Key terms:**
- Context window
- RAG (Retrieval Augmented Generation)
- Semantic search / Vector databases
- Graph RAG / Knowledge graphs
- Context rot
- Evals / Data flywheel
Context engineering is where product strategy meets model behavior. The best AI products aren't using better models—they're using better context.
What is Contextually Aware?
What product managers can actually build with AI today—and where it still breaks.